This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. The human brain is a complex, hierarchical network, in which billions of neurons are interconnected to form functional units that support complex behaviors. Cognitive functions likely arise and are constrained by dynamic neural activity propagating along this hierarchical network. The efficiency, accuracy, and stability of cognitive functions undergo significant development in the first 2 decades of life. Understanding how human functional brain networks develop during this critical developmental period will provide important insights into the relationship between brain function and the emergence of mature cognitive abilities. The goal of the current study is to characterize the development of human functional brain networks using graph-theoretic analyses. We will analyze resting-state functional magnetic resonance imaging (fMRI) data collected from 87 participants ages 8-22 years old. Resting-state fMRI measures the intrinsic, high-amplitude, low-frequency signal fluctuations of the brain, and high correlations among distant brain regions reflect functional connectivity. We will analyze functional brain networks by constructing whole-brain correlation matrices from resting-state fMRI data for each subject. We will further utilize graph-theoretic approaches to characterize the network topology of functional brain networks. Network measures of interest include centrality, path, clustering, efficiency, and small-worldness. Finally, network measures will be compared across age groups to characterize fully the development of functional brain networks. Graph theory analyses will be run using a C++ commandline application developed by our laboratory that utilizes two open-source libraries: GNU Scientific Library and Brain Connectivity ToolboxC++. The application compiles using the GNU C/C++ compiler (version 4.4.4 on our local machines). The application is designed to calculate all metrics of interest for a single subject and is single-threaded. Each subjects dataset is a square matrix approximately 1.5GB in size. The application requires approximately 200 hours to run per subject and uses 1.5-4GB RAM depending upon the calculation. We also hope to utilize the Teragrid supercomputer to calculate the optimal edge density for three aggregated datasets, with each dataset requiring approximately 300 hours to run.